One of the major problems faced by the high-tech manufacturing industry is the need for automated and timely detection of anomalies which can lead to failures of the manufacturing equipment. Failures of the high-tech manufacturing equipment have a direct negative impact on the operating margin and consequently profit of the high-tech manufacturing industry.
Automated and timely detection of anomalies is a difficult problem, the major challenge being the need to understand the interactions between large numbers of machine components. Even very experienced system engineers are not aware of all interactions, especially if those need to be derived from high velocity sensor data. This, in turn, makes it impossible to recognize early warning signals and take action before failure happens.
In particular, high-tech manufacturing equipment, such as photolithography systems or vapor depositions systems, can contain thousands of components which are monitored by hundreds of digital and analog sensors. The sheer size and complexity of the data which is delivered by the sensors monitoring such systems makes it very challenging for system engineers to detect abnormal behavior leading to failures. The two major challenges faced by the system engineers are: (1) the need to know what to monitor and (2) the ability to extract higher level information from raw sensor data in real-time.